We are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle — from model design through deployment, optimization, and ongoing performance in production — and contributes to the technical direction of the AI platform.

Key Responsibilities

• Architect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day one
• Build and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scale
• Own technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainability
• Design and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applications
• Implement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines
• Continuously optimize model performance, inference latency, cost efficiency, and reliability across live systems
• Collaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutions
• Mentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the team
• Contribute to strategic decisions around data architecture, AI infrastructure, and cloud platform direction
• Work with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimization

Requirements

• 7 to 15 or more years of experience in software engineering, data science, or ML engineering
• Strong background in product companies, scale-ups, or enterprise AI platforms
• Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work
• Comfortable owning systems end-to-end from data through model through deployment through monitoring
• Product-first engineering approach, not research-only profiles
• Advanced Python engineering skills with strong systems thinking and a focus on production quality
• Comfortable with fast iteration cycles and deploying models into live environments
• Ability to work directly and confidently with stakeholders and product owners
• Fintech or financial services experience is an advantage

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